國立虎尾科技大學 |

Human-AI Collaboration to Support Mental Health and Well-Being /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Human-AI Collaboration to Support Mental Health and Well-Being // Ashish Sharma.
作者:	Sharma, Ashish,
面頁冊數:	1 electronic resource (339 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
Contained By:	Dissertations Abstracts International86-03B.
標題:	Behavioral psychology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31555939
ISBN:	9798384098287

Human-AI Collaboration to Support Mental Health and Well-Being /
Sharma, Ashish,

Human-AI Collaboration to Support Mental Health and Well-Being /Ashish Sharma. - 1 electronic resource (339 pages)

Source: Dissertations Abstracts International, Volume: 86-03, Section: B.

As mental health conditions surge worldwide, healthcare systems are struggling to provide accessible and high-quality mental health care for all. Although therapy can support people struggling with mental health challenges, barriers like clinician shortages and mental health stigma commonly limit people's access to therapy. In this thesis, I study how human-AI collaboration can improve access to and quality of mental health support.First, I study how human-AI collaboration can empower people who provide support to conduct effective and high-quality conversations. Specifically, I focus on peer supporters on online peer support platforms like Reddit and TalkLife. While peer supporters are motivated and well-intentioned to help support seekers, they are typically untrained and unaware of key psychotherapy skills, such as empathy, that foster effective support. Using a reinforcement learning-based method, evaluated through a randomized trial with 300 peer supporters from the largest peer support platform, I demonstrate that AI-based feedback helps peer supporters express empathy more effectively in their conversations.Second, I investigate how human-AI collaboration can empower people who seek support by making self-guided mental health interventions more accessible and easier to engage with. Self-guided interventions, such as "do-it-yourself" tools to learn and practice coping skills, are often cognitively demanding and emotionally triggering, creating accessibility barriers that limit their wide-scale implementation and adoption. Using cognitive restructuring of negative thoughts as a case study, evaluated through a randomized trial on a large mental health website with 15,531 participants, I show that human-AI collaboration supports people in overcoming negative thoughts and informs psychology theory about processes that lead to positive outcomes.Third, I systematically evaluate human-AI collaboration systems used for mental health support. While there is great interest in utilizing AI for mental health support, there is a significant lack of methods to evaluate their effectiveness, quality, equity, and safety. I study how clinical trials can be conducted to effectively evaluate short-term and long-term outcomes, equity, and safety of AI-based mental health interventions comparing them to traditional approaches. Moreover, I develop a computational framework to automatically assess the behavior of large language models (LLM) when employed as therapists. By analyzing 13 different psychotherapy techniques, I compare the behavior of LLM therapists against that of high- and low-quality human therapy. My analysis reveals that LLMs often resemble behaviors more commonly exhibited in low-quality therapy rather than high-quality therapy, such as offering a higher degree of problem-solving advice when clients share emotions, which is against typical recommendations.My thesis develops two human-AI collaboration systems to support mental health and well-being, along with an evaluation framework for such systems. My work opens opportunities to improve the learning and practice of mental health strategies and coping skills for both support seekers and support providers through human-AI collaboration interventions.

English

ISBN: 9798384098287Subjects--Topical Terms:

1179418
Behavioral psychology.
Subjects--Index Terms:

Behavioral data science

Human-AI Collaboration to Support Mental Health and Well-Being /
LDR:04812nam a22004573i 4500 001 1157855
005 20250603111429.5
006 m o d
007 cr|nu||||||||
008 250804s2024 miu||||||m |||||||eng d
020 $a 9798384098287
035 $a (MiAaPQD)AAI31555939
035 $a AAI31555939
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Sharma, Ashish, $e author. $3 1484138
245 1 0 $a Human-AI Collaboration to Support Mental Health and Well-Being / $c Ashish Sharma.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (339 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
500 $a Advisors: Althoff, Tim Committee members: Smith, Noah A.; Krishna, Ranjay; Hiniker, Alexis; Miner, Adam.
502 $b Ph.D. $c University of Washington $d 2024.
520 $a As mental health conditions surge worldwide, healthcare systems are struggling to provide accessible and high-quality mental health care for all. Although therapy can support people struggling with mental health challenges, barriers like clinician shortages and mental health stigma commonly limit people's access to therapy. In this thesis, I study how human-AI collaboration can improve access to and quality of mental health support.First, I study how human-AI collaboration can empower people who provide support to conduct effective and high-quality conversations. Specifically, I focus on peer supporters on online peer support platforms like Reddit and TalkLife. While peer supporters are motivated and well-intentioned to help support seekers, they are typically untrained and unaware of key psychotherapy skills, such as empathy, that foster effective support. Using a reinforcement learning-based method, evaluated through a randomized trial with 300 peer supporters from the largest peer support platform, I demonstrate that AI-based feedback helps peer supporters express empathy more effectively in their conversations.Second, I investigate how human-AI collaboration can empower people who seek support by making self-guided mental health interventions more accessible and easier to engage with. Self-guided interventions, such as "do-it-yourself" tools to learn and practice coping skills, are often cognitively demanding and emotionally triggering, creating accessibility barriers that limit their wide-scale implementation and adoption. Using cognitive restructuring of negative thoughts as a case study, evaluated through a randomized trial on a large mental health website with 15,531 participants, I show that human-AI collaboration supports people in overcoming negative thoughts and informs psychology theory about processes that lead to positive outcomes.Third, I systematically evaluate human-AI collaboration systems used for mental health support. While there is great interest in utilizing AI for mental health support, there is a significant lack of methods to evaluate their effectiveness, quality, equity, and safety. I study how clinical trials can be conducted to effectively evaluate short-term and long-term outcomes, equity, and safety of AI-based mental health interventions comparing them to traditional approaches. Moreover, I develop a computational framework to automatically assess the behavior of large language models (LLM) when employed as therapists. By analyzing 13 different psychotherapy techniques, I compare the behavior of LLM therapists against that of high- and low-quality human therapy. My analysis reveals that LLMs often resemble behaviors more commonly exhibited in low-quality therapy rather than high-quality therapy, such as offering a higher degree of problem-solving advice when clients share emotions, which is against typical recommendations.My thesis develops two human-AI collaboration systems to support mental health and well-being, along with an evaluation framework for such systems. My work opens opportunities to improve the learning and practice of mental health strategies and coping skills for both support seekers and support providers through human-AI collaboration interventions.
546 $a English
590 $a School code: 0250
650 4 $a Behavioral psychology. $3 1179418
650 4 $a Mental health. $3 564038
650 4 $a Computer engineering. $3 569006
650 4 $a Computer science. $3 573171
653 $a Behavioral data science
653 $a Human-AI collaboration
653 $a Natural language processing
653 $a Large language models
653 $a Psychotherapy techniques
690 $a 0984
690 $a 0464
690 $a 0800
690 $a 0347
690 $a 0384
710 2 $a University of Washington. $b Computer Science and Engineering. $3 1182238
720 1 $a Althoff, Tim $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-03B.
790 $a 0250
791 $a Ph.D.
792 $a 2024
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31555939